M103. Treating Motivation Deficits in Schizophrenia With a Virtual Reality Motivation Training Program
Bibliographic record
Abstract
Background: Motivation deficits have emerged as a critical determinant of functional disability in schizophrenia. Effective therapeutic strategies for motivation deficits, however, remain elusive. This has ultimately hindered our ability to promote recovery for affected individuals. To address this unmet therapeutic need, this open-label pilot study investigated a novel virtual reality-based (VR) training strategy for the treatment of motivation deficits in schizophrenia over the course of 8 weeks of training, along with concomitant effects on community functioning and brain structure and function. Methods: Stable adult outpatients with schizophrenia between the ages of 18 and 35, with prominent motivation deficits were recruited for this study. Participants underwent baseline and post-treatment clinical assessments, evaluation of community functioning with the Quality of Life Scale (QLS), as well as structural (diffusion tensor imaging [DTI]) and functional MRI. Treatment consisted of 8 weeks of VR motivation training using a progressive effort task in a virtual environment for 1 hour per week. Motivation deficits were evaluated using the Apathy Evaluation Scale (AES) every 2 weeks. Our primary outcome of interest was change in AES scores over time, with secondary outcomes consisting of change in overall community functioning (QLS), and frontostriatal white matter microstructural integrity and functioning during motivated behavior. Results: To date, 8 participants have completed this study. Preliminary analyses revealed a significant reduction in AES scores as a result of treatment (F(4,28) = 5.025, P = .004), with on average a 13% reduction in AES score. Participants also exhibited a mean improvement of 29% in QLS score, with 63% of the sample showing more than 20% improvement, although the difference for the overall sample was nonsignificant. Changes in DTI indices of frontostriatal white matter microstructural integrity, and brain function during motivated behavior, as a result of treatment will also be presented. Conclusion: This pilot study investigated the use of a novel VR-based training strategy to treat motivation deficits in young adults with schizophrenia. Preliminary findings suggest that this VR-based training resulted in a significant reduction in the severity of motivation deficits, along with a notable improvement in overall community functioning. With the ongoing search for effective treatments for motivation deficits and their functional consequences in schizophrenia, these findings provide an early promising signal for a potential novel treatment strategy for these deficits that may improve outcomes for patients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".